Predicting functional outcome following acute stroke using a standard clinical examination.

Prescott, R. J.; Garraway, W. M.; Akhtar, Aisha

doi:10.1161/01.str.13.5.641

Cited by 200 publications

(109 citation statements)

References 10 publications

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“…Candidate determinants for development of a prediction model included the following: (1) (11) sensory deficit in the arm as determined by the Thumb-Finding Test (0 to 3 points); 17 (12) Orpington Prognostic Score (1.6 to 6.8 points); (13) homonymous hemianopia 18 (noϭ0/yesϭ1); (14) inattention (1 if Ͼ2 omissions on the letter-cancellation test); 18 (15) conjugate gaze 19 (noϭ0/yesϭ1); (16) social support (noϭ0/yesϭ1); (17) type of (additional) therapy (air splint, arm, or leg training; 0 to 2) 8 ; and (18) severity and extent of paresis of upper and lower extremity motor function in arm and leg as assessed by the Motricity Index (MI) 20 and motor parts of the Fugl-Meyer (FM) score. 21 In the MI, muscle strength was measured for upper extremity (MI arm, 0 to 100) in which 100 points represents normal strength and lower extremity (MI leg, 0 to 100) separately, whereas FM motor scores were subdivided into FM arm score (including wrist, 0 to 52), FM hand score (0 to 14 points), and FM leg score (0 to 34 points) in which the maximum score represents no synergism.…”

Section: Independent Variablesmentioning

confidence: 99%

Probability of Regaining Dexterity in the Flaccid Upper Limb

Kwakkel

Kollen

Grond

et al. 2003

Stroke

1,132

449

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Background and Purpose-To improve the accuracy of early postonset prediction of motor recovery in the flaccid hemiplegic arm, the effects of change in motor function over time on the accuracy of prediction were evaluated, and a prediction model for the probability of regaining dexterity at 6 months was developed. Methods-In 102 stroke patients, dexterity and paresis were measured with the Action Research Arm Test, Motricity Index, and Fugl-Meyer motor evaluation. For model development, 23 candidate determinants were selected. Logistic regression analysis was used for prognostic factors and model development. Results-At 6 months, some dexterity in the paretic arm was found in 38%, and complete functional recovery was seen in 11.6% of the patients. Total anterior circulation infarcts, right hemisphere strokes, homonymous hemianopia, visual gaze deficit, visual inattention, and paresis were statistically significant related to a poor arm function. Motricity Index leg scores of at least 25 points in the first week and Fugl-Meyer arm scores of 11 points in the second week increasing to 19 points in the fourth week raised the probability of developing some dexterity

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Section: Independent Variablesmentioning

confidence: 99%

Probability of Regaining Dexterity in the Flaccid Upper Limb

Kwakkel

Kollen

Grond

et al. 2003

Stroke

1,132

449

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“…Previous investigators have shown that a single model can be applicable to patients seen at any time up to 30 days from stroke onset. 12 The models were developed on 530 of the original 675 patients: 54 patients were excluded because they were not assessed by a study neurologist (50 died before they could be assessed), 58 were seen after 30 days, and 33 had a subarachnoid hemorrhage (SAH).…”

Section: Description Of Training Data Setmentioning

confidence: 99%

Predicting Outcome After Acute and Subacute Stroke

Counsell

Dennis

McDowall

et al. 2002

Stroke

282

159

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Background and Purpose-Statistical models to predict the outcome of patients with acute and subacute stroke could have several uses, but no adequate models exist. We therefore developed and validated new models. Methods-Regression models to predict survival to 30 days after stroke and survival in a nondisabled state at 6 months were produced with the use of established guidelines on 530 patients from a stroke incidence study. Three models were produced for each outcome with progressively more detailed sets of predictor variables collected within 30 days of stroke onset. The models were externally validated and compared on 2 independent cohorts of stroke patients (538 and 1330 patients) by calculating the area under receiver operating characteristic curves (AUC) and by plotting calibration graphs. Results-Models that included only 6 simple variables (age, living alone, independence in activities of daily living before the stroke, the verbal component of the Glasgow Coma Scale, arm power, ability to walk) generally performed as well as more complex models in both validation cohorts (AUC 0.84 to 0.88). They had good calibration but were overoptimistic in patients with the highest predicted probabilities of being independent. There were no differences in AUCs between patients seen within 48 hours of stroke onset and those seen later; between ischemic and hemorrhagic strokes; and between those with and without a previous stroke. Conclusions-The simple models performed well enough to be used for epidemiological purposes such as stratification in trials or correction for case mix. However, clinicians should be cautious about using these models, especially in hyperacute stroke, to influence individual patient management until they have been further evaluated. Further research is required to test whether additional information from brain imaging improves predictive accuracy.

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“…Other studies have shovra that cognitive performance (e.g. memoty, orientation and absttact thought) is associated with success of rehabilitation (Stahelin, 2000;Prescott et al, 1982) and reintegration into the community (Neistadt, 1987).…”

Section: Patient Characteristics and Cognitive Impairmentmentioning

confidence: 99%

Caregiver strain in spouses of stroke patients

Blake¹,

Lincoln²,

Clarke

2003

Clin Rehabil

107

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Objective: To test the ability of a previously generated logistic regression model to predict caregiver strain from carer mood, negative affectivity and perceived patient functional ability. Design: Postal prospective survey. Setting: Spouses of community-residing patients identified from hospital stroke registers. Method: Spouses were assessed at three and six months after stroke. A previously derived equation was used to make predictions at three months of their level of strain at six months, which were compared with observed outcomes. Measures: Spouses were asked to complete the Caregiver Strain Index (CSI), the General Health Questionnaire (GHQ-12), the Positive and Negative Affectivity Schedule (PANAS) and to assess patients' independence in activities of daily living on the Extended Activities of Daily Living Scale (EADL). Results: Of 409 stroke patients, 276 had an identifiable co-resident spouse and 116 (42%) completed the measures. At three months after stroke, 39 carers (34%) were under significant strain with 40 (35%) under strain at six months. The predictive model using the GHQ-12, PANAS and EADL at three months was 78% accurate in predicting levels of caregiver strain at six months. Conclusion: Carers at risk of later strain could be identified for further followup. Services to provide emotional support to carers might be effective in the reduction of carer strain.

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Predicting functional outcome following acute stroke using a standard clinical examination.

Cited by 200 publications

References 10 publications

Probability of Regaining Dexterity in the Flaccid Upper Limb

Probability of Regaining Dexterity in the Flaccid Upper Limb

Predicting Outcome After Acute and Subacute Stroke

Caregiver strain in spouses of stroke patients

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